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<p>As artificial intelligence (AI) becomes integral to higher education, fostering students’ self-regulated learning (SRL) and AI literacy (AIL) is more essential in today’s AI-mediated environment. However, students often exhibit imbalanced SRL and AIL capabilities, which hinders their learning with AI. Our study addresses this gap by identifying distinct learner profiles based on the interplay of SRL and AIL, and examining their developmental characteristics and key differentiating factors. Employing cluster analysis on data from 1,704 Chinese university students, we identified four distinct profiles: Potential (PG), Development (DG), Master (MG), and AI-Inclined (AG). Spearman’s rank correlation was used to assess the internal relationship between SRL and AIL, while the Mann-Whitney U and chi-square tests examined differences in economic resources, device availability, and instructional support. Results reveal significant disparities in SRL-AIL synergy. The MG demonstrated the highest integration, whereas the AG showed over-reliance on AI coupled with low SRL. The PG exhibited a balanced developmental trajectory, while the DG showed inconsistent correlations. Regional economic development, computer access, and integrated instructional guidance emerged as key factors influencing these profiles. Based on these findings, we recommend providing the PG with expansive computer access while offering targeted instructional support to the DG and AG to foster synergistic development. Furthermore, we found that the MG does not represent the final developmental stage. A persistent tension emerges between their advanced AI ethics and practical self-regulatory strategies (e.g., time management). Personalized AI system may help reduce this tension and support a more human-centered AI partnership. This study provides empirical evidence on AI-enhanced learning by delineating learner profiles and their developmental trajectories, offering valuable pedagogical insights for AI education. (269 words)</p>

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Towards human-AI collaborative learning: synergizing self-regulation and artificial-intelligence literacy

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Long Zhang,
  • Shijun Cindy Chen

摘要

As artificial intelligence (AI) becomes integral to higher education, fostering students’ self-regulated learning (SRL) and AI literacy (AIL) is more essential in today’s AI-mediated environment. However, students often exhibit imbalanced SRL and AIL capabilities, which hinders their learning with AI. Our study addresses this gap by identifying distinct learner profiles based on the interplay of SRL and AIL, and examining their developmental characteristics and key differentiating factors. Employing cluster analysis on data from 1,704 Chinese university students, we identified four distinct profiles: Potential (PG), Development (DG), Master (MG), and AI-Inclined (AG). Spearman’s rank correlation was used to assess the internal relationship between SRL and AIL, while the Mann-Whitney U and chi-square tests examined differences in economic resources, device availability, and instructional support. Results reveal significant disparities in SRL-AIL synergy. The MG demonstrated the highest integration, whereas the AG showed over-reliance on AI coupled with low SRL. The PG exhibited a balanced developmental trajectory, while the DG showed inconsistent correlations. Regional economic development, computer access, and integrated instructional guidance emerged as key factors influencing these profiles. Based on these findings, we recommend providing the PG with expansive computer access while offering targeted instructional support to the DG and AG to foster synergistic development. Furthermore, we found that the MG does not represent the final developmental stage. A persistent tension emerges between their advanced AI ethics and practical self-regulatory strategies (e.g., time management). Personalized AI system may help reduce this tension and support a more human-centered AI partnership. This study provides empirical evidence on AI-enhanced learning by delineating learner profiles and their developmental trajectories, offering valuable pedagogical insights for AI education. (269 words)